Dynamic control of urban sewer systems to reduce combined sewer overflows and their adverse impacts

Rathnayake, Upaka; Anwar, Faisal

doi:10.1016/j.jhydrol.2019.124150

Cited by 51 publications

(46 citation statements)

References 84 publications

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“…Bars represent mean concentration from two replicates and whiskers represent the maximum concentration measured. Key: +, storm water and untreated wastewater were combined and released directly into river; *, storm water and treated wastewater were combined and released into environment (reproduced from Munro et al 2019) researchers have developed predictive models to minimise CSO discharges (Zhao et al 2019;Snodgrass et al 2018;Rathnayake and Faisal Anwar 2019). Troutman et al (2020) proposed a load-balancing algorithm to control storage assets within the catchment to improve flow dynamics at the WTP.…”

Section: Mitigation and Treatment Strategies For Emerging Contaminants In Combined Sewer Overflowsmentioning

confidence: 99%

“…For example, Carbone et al ( 2014 ) proposed the use of ‘smart’ gates that adjust themselves during storm events to optimise upstream sewer capacity. Several researchers have developed predictive models to minimise CSO discharges (Zhao et al 2019 ; Snodgrass et al 2018 ; Rathnayake and Faisal Anwar 2019 ). Troutman et al ( 2020 ) proposed a load-balancing algorithm to control storage assets within the catchment to improve flow dynamics at the WTP.…”

Section: Mitigation and Treatment Strategies For Emerging Contaminants In Combined Sewer Overflowsmentioning

confidence: 99%

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A review of combined sewer overflows as a source of wastewater-derived emerging contaminants in the environment and their management

Petrie

2021

Environ Sci Pollut Res

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Emerging contaminants such as pharmaceuticals, illicit drugs and personal care products can be released to the environment in untreated wastewater/stormwater mixtures following storm events. The frequency and intensity of combined sewer overflows (CSOs) has increased in some areas due to increasing urbanisation and climate change. Therefore, this review provides an up-to-date overview on CSOs as an environmental source of emerging contaminants. Other than compounds with high removal, those chiral species subject to enantioselective changes (i.e. degradation or inversion) during wastewater treatment can be effective markers of CSO discharge in the environment. A proposed framework for the selection of emerging contaminants as markers of CSOs is outlined. Studies have demonstrated that CSOs can be the main source of emerging contaminants with high removal efficiency during wastewater treatment (e.g. > 90%). However, the impact of CSOs on the environment is location specific and requires decision-making on their appropriate management at catchment level. This process would be aided by further studies on CSOs which incorporate the monitoring of emerging contaminants and their effects in the environment with those more routinely monitored pollutants (e.g. pathogens and priority substances). Mitigation and treatment strategies for emerging contaminants in CSOs are also discussed.

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Section: Mitigation and Treatment Strategies For Emerging Contaminants In Combined Sewer Overflowsmentioning

confidence: 99%

Section: Mitigation and Treatment Strategies For Emerging Contaminants In Combined Sewer Overflowsmentioning

confidence: 99%

A review of combined sewer overflows as a source of wastewater-derived emerging contaminants in the environment and their management

Petrie

2021

Environ Sci Pollut Res

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“…Retrofitting stormwater systems for active monitoring and real-time control (RTC) is a promising approach to address these issues [10]. RTC has been previously used in other urban infrastructure, such as combined sewers to prevent overflows [11]. While becoming more common with stormwater management, RTC of these systems is not yet the standard.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning with Uncertain Data for Real-Time Stormwater System Control and Flood Mitigation

Saliba

Bowes

Adams

et al. 2020

Water

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Flooding in many areas is becoming more prevalent due to factors such as urbanization and climate change, requiring modernization of stormwater infrastructure. Retrofitting standard passive systems with controllable valves/pumps is promising, but requires real-time control (RTC). One method of automating RTC is reinforcement learning (RL), a general technique for sequential optimization and control in uncertain environments. The notion is that an RL algorithm can use inputs of real-time flood data and rainfall forecasts to learn a policy for controlling the stormwater infrastructure to minimize measures of flooding. In real-world conditions, rainfall forecasts and other state information are subject to noise and uncertainty. To account for these characteristics of the problem data, we implemented Deep Deterministic Policy Gradient (DDPG), an RL algorithm that is distinguished by its capability to handle noise in the input data. DDPG implementations were trained and tested against a passive flood control policy. Three primary cases were studied: (i) perfect data, (ii) imperfect rainfall forecasts, and (iii) imperfect water level and forecast data. Rainfall episodes (100) that caused flooding in the passive system were selected from 10 years of observations in Norfolk, Virginia, USA; 85 randomly selected episodes were used for training and the remaining 15 unseen episodes served as test cases. Compared to the passive system, all RL implementations reduced flooding volume by 70.5% on average, and performed within a range of 5%. This suggests that DDPG is robust to noisy input data, which is essential knowledge to advance the real-world applicability of RL for stormwater RTC.

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“…Therefore, various measures are also taken within the scheme of low impact development (LID) and others for reducing pollutants (Eckart et al, 2018). In the case of combined sewers, serious water pollution can be caused during combined sewer overflow (CSO) events when combined sewage and surface runoff flows exceed the capacity of the sewage treatment plant, or of the maximum flow rate of the system which transmits the combined sources (Rathnayake & Faisal, 2019). CSO management, thus, becomes very important for some older cities where combined sewers are still in service.…”

Section: Discharge Managementmentioning

confidence: 99%

Water cycle management for building water-wise cities

Wang

Luo

2021

Water-Wise Cities and Sustainable Water Systems: Concepts, Technologies, and Applications

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Water is a chemical substance with a simple but stable molecular structure of H 2 O. It cannot be generated or eliminated under normal conditions, so the total mass of water in the whole world is constant. Water itself is transparent, tasteless, odorless, and nearly colorless, but can dissolve various inorganic and organic solutes, and carry fine particulate solids as it flows. All these foreign substances the water carries are called 'impurities' which can enter the water mass and conversely be removed through various natural or artificial actions. Water is essential to human and all living organisms, but any consumption and utilization of water cannot result in any true loss of the water mass. This means that after water use, the water molecules are still there and the only alteration is in their quality, location of existence, and existing state (liquid, gaseous or even solid forms). However, the usability of water may not merely depend on its quantity, but more importantly its quality. Contaminated water cannot be directly supplied for most purposes of water use. In this sense, the problem of water shortage we

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Dynamic control of urban sewer systems to reduce combined sewer overflows and their adverse impacts

Cited by 51 publications

References 84 publications

A review of combined sewer overflows as a source of wastewater-derived emerging contaminants in the environment and their management

A review of combined sewer overflows as a source of wastewater-derived emerging contaminants in the environment and their management

Deep Reinforcement Learning with Uncertain Data for Real-Time Stormwater System Control and Flood Mitigation

Water cycle management for building water-wise cities

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